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ActiveDeeper: A Model-based Active Data Enrichment System

Summary: ActiveDeeper uses the deep web as a labeler to train a data-enrichment model for local databases. Model-driven enrichment from deep-web data outperforms state-of-the-art in real-world settings; delivered as a Google Sheets add-on. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12342
Venue
VLDB
Year
2020
Pagerank
5.2843006e-05
Overall Rank
9,348 | 35.87%
DOI
10.14778/3415478.3415500

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhao_vldb20,
        title = {{ActiveDeeper: A Model-based Active Data Enrichment System}},
        author = {Zhao, Liang and Li, Qingcan and Wang, Pei and Wang, Jiannan and Wu, Eugene},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {12},
        pages = {2885--2888},
        doi = {10.14778/3415478.3415500},
        url = {https://doi.org/10.14778/3415478.3415500},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
9,647 Rock: Cleaning Data by Embedding ML in Logic Rules 2024 SIGMOD 5.2430158e-05
10,324 Outliers: The Good, the Bad and the Ugly 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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